Euler-Maruyama and Kloeden-Platen-Schurz computing paradigm for stochastic vector-borne plant epidemic model

Abstract

The dynamics of viral infection within-plant hosts are of critical importance for characterizing the prevalence and impact of plant diseases. However, few mathematical modeling efforts have been made to characterize viral dynamics within plants. In this study, the dynamics of the vector-borne plant epidemic (VBPE) model are modeled with two nonlinear mathematical models, the deterministic vector-borne plant epidemic (DVBPE) model and the stochastic vector-borne plant epidemic (SVBPE) model to portray and forecast the virus dynamical behavior. The VBPE model segregated the population into three classes: susceptible plants (S), infectious plants (I), and infectious vectors (Y). For classes S, I, and Y, the approximate solution is established by creating a sufficient number of scenarios by varying the ratio of infection, infected vector biting rate, infection rates of plant model, host’s capacity of infection, disease-induced death rate of infectious hosts, insect vectors’ natural mortality rate, the stochastic term for susceptible plants, the stochastic term for infected plants and stochastic term for infected vectors The Adams method is utilized to determine the approximate solution for the DVBPE model, while the Euler-Maruyama and Kloeden-Platen-Schurz methods are used to evaluate the SVBPE model. Finally, a comparison between the DVBPE and SVBPE models is presented.